Since sharp jumps are frequently found in real-world data, making machine-learning models that can learn sharp jumps is a significant challenge in interpretable models [1]}, [2]}.
For example, [1]} found that the average house prices highly jump in San Francisco and Los Angeles in the California Housing dataset.
In addition, [2]} found that patients' mortality risks significantly dropped at PFratio=332 because they imputed missing values by the mean 332 (healthy patients commonly do not check PFratio).
However, neural networks struggle to capture such sharp jumps [1]} because of the bias towards smooth functions [6]}, [7]}.
